In [ ]:
import requests
from zipfile import ZipFile
from io import BytesIO
import pandas as pd
import numpy as np
from google.colab import files

Need to upload the Zip Code or Contaminant (or UCMR3_5_Combo_with_ZIPCODES) csv file you want to use everytime this is run.

In [ ]:
# files.upload()
In [ ]:
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

Make sure this is the Zip Code or Contaminant csv file you want.

In [ ]:
#df = pd.read_csv('UCMR_3_5_Combo_with_ZIPCODES.csv') # very large dataset- over 6.6 million rows
df = pd.read_csv('/content/drive/MyDrive/Z Colab Notebooks/small_UCMR_3_5_Combo_with_ZIPCODES.csv')
#df.head(50)
df.head(9999)
Out[ ]:
CollectionDate Contaminant AnalyticalResultValue ZIPCODE Latitude Longitude
0 1.426550e+09 chlorate 160.0000 6339 41.44408 -71.99851
1 1.426550e+09 strontium 72.0000 6339 41.44408 -71.99851
2 1.434586e+09 chlorate 340.0000 6339 41.44408 -71.99851
3 1.434586e+09 strontium 66.0000 6339 41.44408 -71.99851
4 1.444003e+09 strontium 62.0000 6339 41.44408 -71.99851
... ... ... ... ... ... ...
370 1.727309e+09 PFHxA 0.0085 35005 33.59215 -86.99833
371 1.727309e+09 PFBS 0.0055 35005 33.59215 -86.99833
372 1.686787e+09 PFBS 0.0033 48858 43.61483 -84.79576
373 1.701821e+09 PFBS 0.0048 48858 43.61483 -84.79576
374 1.686787e+09 lithium 9.4300 48858 43.61483 -84.79576

375 rows × 6 columns

Create heatmaps based on latitude, longitude, AnalyticalResultValue.

In [ ]:
import folium
from folium.plugins import HeatMap
import os

def save_heatmap_to_file(m, file_name):
    """Saves a Folium map object as an HTML file.

    Args:
        m (folium.folium.Map): The Folium map object to save.
        file_name (str): The name of the file to save the map to.
    """
    m.save(file_name)

# Create an 'output_heatmaps' directory if it doesn't exist
output_dir = 'output_heatmaps_no_blanks'
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

For small_UCRM_3_5_Combo_with_ZIPCODES.csv which has six zip codes out of 18,601 zip codes: Create one heatmap of all contaminants based on latitude, longitude, AnalyticalResultValue. Can be used for all 82 heatmaps if using the large csv file UCMR_3_5_Combo_with_ZIPCODES.csv

In [ ]:
# This is too much data for all 82 contaminants, keeps crashing. Can do smaller zip codes data instead.
print(f"Generating heatmap for all contaminants based on location and AnalyticalResultValue.\n")

# Prepare data for this heatmap using vectorized operations
heatmap_data = df[['Latitude', 'Longitude', 'AnalyticalResultValue']].values.tolist()

#Create a folium map centered on the US
m = folium.Map(location=[37.8, -96.9], zoom_start=4)

# Add a heatmap layer using Latitude, Longitude, and concentration (MRL)
HeatMap(heatmap_data, min_opacity=0.2, radius=10, blur=10, max_zoom=1).add_to(m)

display(m)
print("\n")

# Create file name and save the map
# file_name = os.path.join(output_dir, f"all_heatmap.html")
# save_heatmap_to_file(m, file_name)

Generating heatmap for all contaminants based on location and AnalyticalResultValue.
Make this Notebook Trusted to load map: File -> Trust Notebook

For UCMR_3_5_Combo_with_ZIPCODES.csv: Create 85 heatmaps for each contaminant based on latitude, longitude, MRLs. Otherwise will create heatmaps with any other csv we are using.

In [ ]:
i = 0
for contaminant, group_data in sorted(df.groupby('Contaminant')):
    print(f"Generating heatmap for Contaminant: {contaminant}\n")

    # Prepare data for this heatmap
    heatmap_data = group_data[['Latitude', 'Longitude', 'AnalyticalResultValue']].values

    # Create a folium map centered on the US
    m = folium.Map(location=[37.8, -96.9], zoom_start=4)

    # Add a heatmap layer using Latitude, Longitude, and concentration (MRL)
    HeatMap(heatmap_data, min_opacity=0.2, radius=10, blur=10, max_zoom=1).add_to(m)

    display(m)
    print("\n")

    # Create file name and save the map
    file_name = os.path.join(output_dir, f"{contaminant}_heatmap.html")
    save_heatmap_to_file(m, file_name)

    i += 1

print(f"Heatmaps saved to the '{output_dir}' directory.")

Generating heatmap for Contaminant: 1,4-dioxane
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: 2-methoxyethanol
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: 6:2 FTS
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: HAA5
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: HAA6Br
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: HAA9
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: PFBS
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: PFHxA
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: PFOA
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: PFPeA
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: chlorate
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: chromium
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: chromium-6
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: cobalt
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: lithium
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: manganese
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: molybdenum
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: strontium
Make this Notebook Trusted to load map: File -> Trust Notebook

Generating heatmap for Contaminant: vanadium
Make this Notebook Trusted to load map: File -> Trust Notebook

Heatmaps saved to the 'output_heatmaps_no_blanks' directory.

Uncomment this section if wanting to save heatmaps in a zip file.

In [ ]:
# import shutil
# shutil.make_archive('output_heatmaps', 'zip', 'output_heatmaps')
# from google.colab import files
# files.download('output_heatmaps.zip')

Linear Regression training and prediction This crashes with all of the zip codes. Just use a few zip codes at a time.

In [ ]:
zipcode_data = sorted(df.groupby('ZIPCODE'))
In [ ]:
def optimize_zipcode_data_for_time_series_optimized(df):
    """
    Optimizes zip code data for time series analysis,
    grouping collection dates for a specific contaminant's AnalyticalResultValue.

    Args:
        df (pd.DataFrame): The input DataFrame.

    Returns:
        dict: A nested dictionary containing time series data.
              {zipcode: {contaminant: {analyticalResultValue: [collection_date, ...]}}}
    """

    df = df.sort_values(by=['ZIPCODE', 'CollectionDate'])
    zipcode_dict = {}

    for _, row in df.iterrows():
        zipcode = row['ZIPCODE']
        contaminant = row['Contaminant']
        collection_date = row['CollectionDate']
        analyticalResultValue = row['AnalyticalResultValue']
        print(row)

        zipcode_dict.setdefault(zipcode, {}).setdefault(contaminant, {}).setdefault(analyticalResultValue, []).append(collection_date)

    return zipcode_dict

# Example usage:
zipcode_dict = optimize_zipcode_data_for_time_series_optimized(df)

CollectionDate           1426550400.0
Contaminant                  chlorate
AnalyticalResultValue           160.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 0, dtype: object
CollectionDate           1426550400.0
Contaminant                 strontium
AnalyticalResultValue            72.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 1, dtype: object
CollectionDate           1426550400.0
Contaminant                  chlorate
AnalyticalResultValue            30.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 9, dtype: object
CollectionDate           1426550400.0
Contaminant                  vanadium
AnalyticalResultValue             0.2
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 10, dtype: object
CollectionDate           1426550400.0
Contaminant                  chromium
AnalyticalResultValue             0.2
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 11, dtype: object
CollectionDate           1426550400.0
Contaminant                    cobalt
AnalyticalResultValue             1.4
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 12, dtype: object
CollectionDate           1426550400.0
Contaminant                chromium-6
AnalyticalResultValue            0.17
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 13, dtype: object
CollectionDate           1426550400.0
Contaminant                 strontium
AnalyticalResultValue           250.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 14, dtype: object
CollectionDate           1434585600.0
Contaminant                  chlorate
AnalyticalResultValue           340.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 2, dtype: object
CollectionDate           1434585600.0
Contaminant                 strontium
AnalyticalResultValue            66.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 3, dtype: object
CollectionDate           1434585600.0
Contaminant                    cobalt
AnalyticalResultValue             1.5
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 15, dtype: object
CollectionDate           1434585600.0
Contaminant                chromium-6
AnalyticalResultValue            0.14
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 16, dtype: object
CollectionDate           1434585600.0
Contaminant                  vanadium
AnalyticalResultValue             0.2
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 17, dtype: object
CollectionDate           1434585600.0
Contaminant                  chlorate
AnalyticalResultValue           110.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 18, dtype: object
CollectionDate           1434585600.0
Contaminant                 strontium
AnalyticalResultValue           270.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 19, dtype: object
CollectionDate           1444003200.0
Contaminant                 strontium
AnalyticalResultValue            62.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 4, dtype: object
CollectionDate           1444003200.0
Contaminant                chromium-6
AnalyticalResultValue            0.04
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 5, dtype: object
CollectionDate           1444003200.0
Contaminant                  chlorate
AnalyticalResultValue           470.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 6, dtype: object
CollectionDate           1444003200.0
Contaminant                  chlorate
AnalyticalResultValue           170.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 20, dtype: object
CollectionDate           1444003200.0
Contaminant                  vanadium
AnalyticalResultValue             0.2
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 21, dtype: object
CollectionDate           1444003200.0
Contaminant                chromium-6
AnalyticalResultValue            0.06
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 22, dtype: object
CollectionDate           1444003200.0
Contaminant                 strontium
AnalyticalResultValue           260.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 23, dtype: object
CollectionDate           1444003200.0
Contaminant                    cobalt
AnalyticalResultValue             1.3
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 24, dtype: object
CollectionDate           1450137600.0
Contaminant                 strontium
AnalyticalResultValue            66.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 7, dtype: object
CollectionDate           1450137600.0
Contaminant                  chlorate
AnalyticalResultValue           370.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 8, dtype: object
CollectionDate           1450137600.0
Contaminant                 strontium
AnalyticalResultValue           270.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 25, dtype: object
CollectionDate           1450137600.0
Contaminant                    cobalt
AnalyticalResultValue             1.4
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 26, dtype: object
CollectionDate           1450137600.0
Contaminant                  chlorate
AnalyticalResultValue            74.0
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 27, dtype: object
CollectionDate           1450137600.0
Contaminant                chromium-6
AnalyticalResultValue            0.17
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 28, dtype: object
CollectionDate           1450137600.0
Contaminant                  vanadium
AnalyticalResultValue             0.2
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 29, dtype: object
CollectionDate           1450137600.0
Contaminant                  chromium
AnalyticalResultValue             0.2
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 30, dtype: object
CollectionDate           1522627200.0
Contaminant                 manganese
AnalyticalResultValue           4.976
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 235, dtype: object
CollectionDate           1522627200.0
Contaminant                    HAA6Br
AnalyticalResultValue          11.408
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 239, dtype: object
CollectionDate           1522627200.0
Contaminant                      HAA9
AnalyticalResultValue          40.535
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 240, dtype: object
CollectionDate           1522627200.0
Contaminant                      HAA5
AnalyticalResultValue          29.643
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 241, dtype: object
CollectionDate           1522627200.0
Contaminant                    HAA6Br
AnalyticalResultValue          10.822
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 251, dtype: object
CollectionDate           1522627200.0
Contaminant                      HAA9
AnalyticalResultValue          36.732
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 252, dtype: object
CollectionDate           1522627200.0
Contaminant                      HAA5
AnalyticalResultValue          26.422
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 253, dtype: object
CollectionDate           1528070400.0
Contaminant                 manganese
AnalyticalResultValue          18.045
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 236, dtype: object
CollectionDate           1528070400.0
Contaminant                    HAA6Br
AnalyticalResultValue          10.543
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 242, dtype: object
CollectionDate           1528070400.0
Contaminant                      HAA9
AnalyticalResultValue          52.079
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 243, dtype: object
CollectionDate           1528070400.0
Contaminant                      HAA5
AnalyticalResultValue          42.602
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 244, dtype: object
CollectionDate           1528070400.0
Contaminant                    HAA6Br
AnalyticalResultValue          11.057
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 254, dtype: object
CollectionDate           1528070400.0
Contaminant                      HAA9
AnalyticalResultValue          52.347
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 255, dtype: object
CollectionDate           1528070400.0
Contaminant                      HAA5
AnalyticalResultValue           42.34
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 256, dtype: object
CollectionDate           1536019200.0
Contaminant                 manganese
AnalyticalResultValue          27.482
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 237, dtype: object
CollectionDate           1536019200.0
Contaminant                    HAA6Br
AnalyticalResultValue           8.943
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 245, dtype: object
CollectionDate           1536019200.0
Contaminant                      HAA9
AnalyticalResultValue          23.006
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 246, dtype: object
CollectionDate           1536019200.0
Contaminant                      HAA5
AnalyticalResultValue          15.501
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 247, dtype: object
CollectionDate           1536019200.0
Contaminant                    HAA6Br
AnalyticalResultValue           9.137
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 257, dtype: object
CollectionDate           1536019200.0
Contaminant                      HAA9
AnalyticalResultValue          27.152
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 258, dtype: object
CollectionDate           1536019200.0
Contaminant                      HAA5
AnalyticalResultValue          19.231
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 259, dtype: object
CollectionDate           1543881600.0
Contaminant                 manganese
AnalyticalResultValue           8.949
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 238, dtype: object
CollectionDate           1543881600.0
Contaminant                    HAA6Br
AnalyticalResultValue          11.502
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 248, dtype: object
CollectionDate           1543881600.0
Contaminant                      HAA9
AnalyticalResultValue          29.425
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 249, dtype: object
CollectionDate           1543881600.0
Contaminant                      HAA5
AnalyticalResultValue          19.545
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 250, dtype: object
CollectionDate           1543881600.0
Contaminant                    HAA6Br
AnalyticalResultValue          11.277
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 260, dtype: object
CollectionDate           1543881600.0
Contaminant                      HAA9
AnalyticalResultValue          32.134
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 261, dtype: object
CollectionDate           1543881600.0
Contaminant                      HAA5
AnalyticalResultValue          22.223
ZIPCODE                          6339
Latitude                     41.44408
Longitude                   -71.99851
Name: 262, dtype: object
CollectionDate           1426032000.0
Contaminant                chromium-6
AnalyticalResultValue          0.0565
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 54, dtype: object
CollectionDate           1426032000.0
Contaminant               1,4-dioxane
AnalyticalResultValue           0.163
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 55, dtype: object
CollectionDate           1426032000.0
Contaminant                 strontium
AnalyticalResultValue            34.7
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 56, dtype: object
CollectionDate           1426032000.0
Contaminant                  chlorate
AnalyticalResultValue            80.5
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 57, dtype: object
CollectionDate           1426032000.0
Contaminant                chromium-6
AnalyticalResultValue          0.0654
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 73, dtype: object
CollectionDate           1426032000.0
Contaminant                 strontium
AnalyticalResultValue            40.7
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 74, dtype: object
CollectionDate           1426032000.0
Contaminant                  chlorate
AnalyticalResultValue            70.0
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 75, dtype: object
CollectionDate           1434585600.0
Contaminant                molybdenum
AnalyticalResultValue             2.7
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 58, dtype: object
CollectionDate           1434585600.0
Contaminant                chromium-6
AnalyticalResultValue             0.1
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 59, dtype: object
CollectionDate           1434585600.0
Contaminant               1,4-dioxane
AnalyticalResultValue            0.09
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 60, dtype: object
CollectionDate           1434585600.0
Contaminant                  vanadium
AnalyticalResultValue            0.68
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 61, dtype: object
CollectionDate           1434585600.0
Contaminant                 strontium
AnalyticalResultValue            61.0
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 62, dtype: object
CollectionDate           1434585600.0
Contaminant                molybdenum
AnalyticalResultValue            2.63
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 76, dtype: object
CollectionDate           1434585600.0
Contaminant                chromium-6
AnalyticalResultValue          0.0838
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 77, dtype: object
CollectionDate           1434585600.0
Contaminant                  vanadium
AnalyticalResultValue           0.664
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 78, dtype: object
CollectionDate           1434585600.0
Contaminant                 strontium
AnalyticalResultValue            59.9
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 79, dtype: object
CollectionDate           1441843200.0
Contaminant                chromium-6
AnalyticalResultValue            0.09
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 63, dtype: object
CollectionDate           1441843200.0
Contaminant                  vanadium
AnalyticalResultValue             0.5
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 64, dtype: object
CollectionDate           1441843200.0
Contaminant                 strontium
AnalyticalResultValue            43.0
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 65, dtype: object
CollectionDate           1441843200.0
Contaminant                 manganese
AnalyticalResultValue             1.1
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 66, dtype: object
CollectionDate           1441843200.0
Contaminant                chromium-6
AnalyticalResultValue             0.1
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 80, dtype: object
CollectionDate           1441843200.0
Contaminant                  vanadium
AnalyticalResultValue             0.5
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 81, dtype: object
CollectionDate           1441843200.0
Contaminant                 strontium
AnalyticalResultValue            51.0
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 82, dtype: object
CollectionDate           1441843200.0
Contaminant                  chlorate
AnalyticalResultValue            78.0
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 83, dtype: object
CollectionDate           1441843200.0
Contaminant                 manganese
AnalyticalResultValue             1.1
ZIPCODE                         35005
Latitude                     33.59215
Longitude                   -86.99833
Name: 84, dtype: object
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Contaminant                      HAA5
AnalyticalResultValue             9.8
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 327, dtype: object
CollectionDate           1526860800.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.5
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 328, dtype: object
CollectionDate           1526860800.0
Contaminant                      HAA5
AnalyticalResultValue            11.7
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 338, dtype: object
CollectionDate           1526860800.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.5
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 339, dtype: object
CollectionDate           1526860800.0
Contaminant                      HAA9
AnalyticalResultValue            12.2
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 340, dtype: object
CollectionDate           1526860800.0
Contaminant                      HAA5
AnalyticalResultValue            10.7
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 350, dtype: object
CollectionDate           1526860800.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.5
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 351, dtype: object
CollectionDate           1526860800.0
Contaminant                      HAA9
AnalyticalResultValue            11.2
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 352, dtype: object
CollectionDate           1526860800.0
Contaminant                 manganese
AnalyticalResultValue             2.4
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 362, dtype: object
CollectionDate           1534723200.0
Contaminant                      HAA9
AnalyticalResultValue             8.9
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 317, dtype: object
CollectionDate           1534723200.0
Contaminant                      HAA5
AnalyticalResultValue             8.1
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 318, dtype: object
CollectionDate           1534723200.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.8
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 319, dtype: object
CollectionDate           1534723200.0
Contaminant                      HAA9
AnalyticalResultValue            14.1
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 329, dtype: object
CollectionDate           1534723200.0
Contaminant                      HAA5
AnalyticalResultValue            13.3
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 330, dtype: object
CollectionDate           1534723200.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.8
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 331, dtype: object
CollectionDate           1534723200.0
Contaminant                      HAA5
AnalyticalResultValue            12.1
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 341, dtype: object
CollectionDate           1534723200.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.9
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 342, dtype: object
CollectionDate           1534723200.0
Contaminant                      HAA9
AnalyticalResultValue            13.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 343, dtype: object
CollectionDate           1534723200.0
Contaminant                      HAA5
AnalyticalResultValue             8.2
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 353, dtype: object
CollectionDate           1534723200.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.8
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 354, dtype: object
CollectionDate           1534723200.0
Contaminant                      HAA9
AnalyticalResultValue             9.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 355, dtype: object
CollectionDate           1534723200.0
Contaminant                 manganese
AnalyticalResultValue             9.8
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 363, dtype: object
CollectionDate           1541721600.0
Contaminant                      HAA9
AnalyticalResultValue            12.5
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 320, dtype: object
CollectionDate           1541721600.0
Contaminant                      HAA5
AnalyticalResultValue            12.5
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 321, dtype: object
CollectionDate           1541721600.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 322, dtype: object
CollectionDate           1541721600.0
Contaminant                      HAA9
AnalyticalResultValue            18.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 332, dtype: object
CollectionDate           1541721600.0
Contaminant                      HAA5
AnalyticalResultValue            17.7
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 333, dtype: object
CollectionDate           1541721600.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.3
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 334, dtype: object
CollectionDate           1541721600.0
Contaminant                      HAA5
AnalyticalResultValue            19.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 344, dtype: object
CollectionDate           1541721600.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 345, dtype: object
CollectionDate           1541721600.0
Contaminant                      HAA9
AnalyticalResultValue            19.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 346, dtype: object
CollectionDate           1541721600.0
Contaminant                      HAA5
AnalyticalResultValue            13.3
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 356, dtype: object
CollectionDate           1541721600.0
Contaminant                    HAA6Br
AnalyticalResultValue             0.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 357, dtype: object
CollectionDate           1541721600.0
Contaminant                      HAA9
AnalyticalResultValue            13.3
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 358, dtype: object
CollectionDate           1541721600.0
Contaminant                 manganese
AnalyticalResultValue             4.1
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 364, dtype: object
CollectionDate           1550102400.0
Contaminant                      HAA9
AnalyticalResultValue            11.3
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 323, dtype: object
CollectionDate           1550102400.0
Contaminant                      HAA5
AnalyticalResultValue            10.3
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 324, dtype: object
CollectionDate           1550102400.0
Contaminant                    HAA6Br
AnalyticalResultValue             1.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 325, dtype: object
CollectionDate           1550102400.0
Contaminant                    HAA6Br
AnalyticalResultValue             1.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 335, dtype: object
CollectionDate           1550102400.0
Contaminant                      HAA5
AnalyticalResultValue            12.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 336, dtype: object
CollectionDate           1550102400.0
Contaminant                      HAA9
AnalyticalResultValue            13.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 337, dtype: object
CollectionDate           1550102400.0
Contaminant                      HAA5
AnalyticalResultValue            13.7
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 347, dtype: object
CollectionDate           1550102400.0
Contaminant                    HAA6Br
AnalyticalResultValue             1.2
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 348, dtype: object
CollectionDate           1550102400.0
Contaminant                      HAA9
AnalyticalResultValue            14.9
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 349, dtype: object
CollectionDate           1550102400.0
Contaminant                      HAA5
AnalyticalResultValue            10.9
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 359, dtype: object
CollectionDate           1550102400.0
Contaminant                    HAA6Br
AnalyticalResultValue             1.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 360, dtype: object
CollectionDate           1550102400.0
Contaminant                      HAA9
AnalyticalResultValue            11.9
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 361, dtype: object
CollectionDate           1550102400.0
Contaminant                 manganese
AnalyticalResultValue             3.0
ZIPCODE                         98221
Latitude                     48.50401
Longitude                  -122.65435
Name: 365, dtype: object
In [ ]:
first_entry = list(zipcode_dict.items())[0]
print(first_entry)

import json

with open('zipcode_data.json', 'w') as f:
    json.dump(zipcode_dict, f, indent=4)

(6339, {'chlorate': {160.0: [1426550400.0], 30.0: [1426550400.0], 340.0: [1434585600.0], 110.0: [1434585600.0], 470.0: [1444003200.0], 170.0: [1444003200.0], 370.0: [1450137600.0], 74.0: [1450137600.0]}, 'strontium': {72.0: [1426550400.0], 250.0: [1426550400.0], 66.0: [1434585600.0, 1450137600.0], 270.0: [1434585600.0, 1450137600.0], 62.0: [1444003200.0], 260.0: [1444003200.0]}, 'vanadium': {0.2: [1426550400.0, 1434585600.0, 1444003200.0, 1450137600.0]}, 'chromium': {0.2: [1426550400.0, 1450137600.0]}, 'cobalt': {1.4: [1426550400.0, 1450137600.0], 1.5: [1434585600.0], 1.3: [1444003200.0]}, 'chromium-6': {0.17: [1426550400.0, 1450137600.0], 0.14: [1434585600.0], 0.04: [1444003200.0], 0.06: [1444003200.0]}, 'manganese': {4.976: [1522627200.0], 18.045: [1528070400.0], 27.482: [1536019200.0], 8.949: [1543881600.0]}, 'HAA6Br': {11.408: [1522627200.0], 10.822: [1522627200.0], 10.543: [1528070400.0], 11.057: [1528070400.0], 8.943: [1536019200.0], 9.137: [1536019200.0], 11.502: [1543881600.0], 11.277: [1543881600.0]}, 'HAA9': {40.535: [1522627200.0], 36.732: [1522627200.0], 52.079: [1528070400.0], 52.347: [1528070400.0], 23.006: [1536019200.0], 27.152: [1536019200.0], 29.425: [1543881600.0], 32.134: [1543881600.0]}, 'HAA5': {29.643: [1522627200.0], 26.422: [1522627200.0], 42.602: [1528070400.0], 42.34: [1528070400.0], 15.501: [1536019200.0], 19.231: [1536019200.0], 19.545: [1543881600.0], 22.223: [1543881600.0]}})
In [ ]:
import os
import heapq
import datetime
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor  # Import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split

def train_model_per_zipcode(zipcode_dict):
    """Trains a Random Forest Regression model for each zip code and contaminant.
       Stores predicted AnalyticalResultValues for future date and returns a list of tuples
       containing (zipcode, contaminant, predicted_AnalyticalResultValue).

    Args:
        zipcode_dict: A dictionary with zip codes as keys and inner dictionaries
                      containing contaminant data (collection date and AnalyticalResultValue).
    """

    models = {}
    top_graphs = []  # Priority queue to store top graphs (abs(r2_score - 1), file_name)
    graph_count = 0
    predicted_AnalyticalValues = []  # List to store (zipcode, contaminant, predicted_AnalyticalResultValue)

    # Create an 'output_graphs' directory if it doesn't exist
    output_dir = 'output_graphs'
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    for zipcode, contaminant_data in zipcode_dict.items():
        for contaminant, data_points in contaminant_data.items():  # Iterate through contaminants
            X = []
            y = []
            # Changed loop to iterate through the actual structure of data_points
            for analyticalResultValue, collection_dates in data_points.items():
                for collection_date in collection_dates:
                    X.append([collection_date])  # Use collection date as the feature
                    y.append(analyticalResultValue)  # Use analyticalResultValue as the target

            if len(X) > 1:  # Need at least 2 data points for training
                X_train, X_test, y_train, y_test = train_test_split(
                    X, y, test_size=0.2, random_state=42
                )

                # Create and train a RandomForestRegressor model
                model = RandomForestRegressor(n_estimators=100, random_state=42)
                model.fit(X_train, y_train)

                y_pred = model.predict(X_test)

                # Calculate and print the MSE for this model
                mse = mean_squared_error(y_test, y_pred)
                print(f"\nZip Code: {zipcode}, MSE: {mse}")

                # Calculate and print the R-squared score
                r2 = r2_score(y_test, y_pred)
                if np.isnan(r2):
                    r2 = 1.0
                print(f"Zip Code: {zipcode}, R^2 score: {r2}")

                models[zipcode] = model  # Store the model for this zip code

                # --- Prediction into the future ---
                future_date = datetime.datetime.now() + datetime.timedelta(days=365)
                future_date_timestamp = int(future_date.timestamp())
                future_prediction = model.predict([[future_date_timestamp]])
                print(f"Prediction for {contaminant} in zip code {zipcode} on {future_date.strftime('%Y-%m-%d')}: {future_prediction[0]}")

                # Store the prediction
                predicted_AnalyticalValues.append((zipcode, contaminant, future_prediction[0], future_date.strftime('%Y-%m-%d')))

                # --- Plotting ---
                X_dates = [datetime.datetime.fromtimestamp(ts) for ts in np.array(X).flatten()]
                plt.figure(figsize=(8, 6))
                plt.scatter(X_dates, y, color='blue', label='Actual AnalyticalResultValue')
                plt.plot(X_dates, model.predict(np.array(X).reshape(-1, 1)), color='red', label='Predicted AnalyticalResultValue')

                # Add future prediction to the plot
                plt.scatter([future_date], future_prediction, color='green', marker='o', s=100, label='Future Prediction')

                plt.title(f"Random Forest Regression for Zip Code: {zipcode} and Contaminant: {contaminant}\n"
          f"Prediction for {future_date.strftime('%Y-%m-%d')}: {future_prediction[0]}\n"
          f"Mean Squared Error: {mse}, R^2 Score: {r2}")
                plt.xlabel('Collection Date')
                plt.ylabel('AnalyticalResultValue')
                plt.xticks(rotation=45, ha='right')
                plt.legend()
                plt.grid(True)
                plt.tight_layout()

                # --- Saving Graphs (with variety selection) ---
                file_name = os.path.join(output_dir, f"{zipcode}_{contaminant}_graph.png")
                # plt.savefig(file_name)  # Save the graph - might not be necessary unless you want a lot

                # Calculate and print the R-squared score
                r2 = r2_score(y_test, y_pred)
                if np.isnan(r2):
                    r2 = 1.0  # Handle NaN by setting R^2 to 1.0

                #print(f"Zip Code: {zipcode}, R^2 score: {r2}")
                accuracy = abs(r2 - 1)
                models[zipcode] = model  # Store the model for this zip code

                # Calculate variety (standard deviation of MRL values)
                variety = np.std(y)

                # Add to priority queue based on variety and accuracy
                score = variety * (1 - accuracy) # Combine variety and accuracy
                heapq.heappush(top_graphs, (-score, file_name))  # Use negative for max-heap

                if len(top_graphs) > 5:  # Keep only top 5
                    heapq.heappop(top_graphs)

                graph_count += 1
                plt.show()

    # Save the top 15 most accurate graphs
    print(f"Saving top 15 most accurate predictions based on R^2 score out of {graph_count} total graphs")
    for accuracy, file_name in top_graphs:
        plt.savefig(file_name)

    print(f"Total graphs generated: {graph_count}")
    return models, predicted_AnalyticalValues



# Train models for each zip code
models, predicted_AnalyticalValues = train_model_per_zipcode(zipcode_dict)

# You can now use these models to make predictions for future collection dates
# for each zip code.
print(models)

Zip Code: 6339, MSE: 32670.10442244444
Zip Code: 6339, R^2 score: -5.667368249478457
Prediction for chlorate in zip code 6339 on 2025-11-12: 239.09

Zip Code: 6339, MSE: 26665.292192499997
Zip Code: 6339, R^2 score: -265.652921925
Prediction for strontium in zip code 6339 on 2025-11-12: 92.69200000000001

Zip Code: 6339, MSE: 1.3019286424057714e-31
Zip Code: 6339, R^2 score: 1.0
Prediction for vanadium in zip code 6339 on 2025-11-12: 0.19999999999999965

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 6339, MSE: 1.509929076399593e-31
Zip Code: 6339, R^2 score: 1.0
Prediction for chromium in zip code 6339 on 2025-11-12: 0.19999999999999962

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 6339, MSE: 0.0025000000000001154
Zip Code: 6339, R^2 score: 1.0
Prediction for cobalt in zip code 6339 on 2025-11-12: 1.3499999999999988

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 6339, MSE: 0.013171387777777785
Zip Code: 6339, R^2 score: 1.0
Prediction for chromium-6 in zip code 6339 on 2025-11-12: 0.055233333333333315

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 6339, MSE: 43.14742556889999
Zip Code: 6339, R^2 score: 1.0
Prediction for manganese in zip code 6339 on 2025-11-12: 12.787879999999998

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 6339, MSE: 0.35435278561671835
Zip Code: 6339, R^2 score: 0.5007753374717131
Prediction for HAA6Br in zip code 6339 on 2025-11-12: 11.202760000000001

Zip Code: 6339, MSE: 24.179281688917285
Zip Code: 6339, R^2 score: -0.053834392672508224
Prediction for HAA9 in zip code 6339 on 2025-11-12: 30.67623000000002

Zip Code: 6339, MSE: 23.250461824147912
Zip Code: 6339, R^2 score: -0.798510582343871
Prediction for HAA5 in zip code 6339 on 2025-11-12: 20.953629999999997

Zip Code: 35005, MSE: 2.249639711999923e-05
Zip Code: 35005, R^2 score: 0.8513028149910817
Prediction for chromium-6 in zip code 35005 on 2025-11-12: 0.060850000000000036

Zip Code: 35005, MSE: 0.002683550809000013
Zip Code: 35005, R^2 score: 1.0
Prediction for 1,4-dioxane in zip code 35005 on 2025-11-12: 0.16129899999999964

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 35005, MSE: 2.737578952668344
Zip Code: 35005, R^2 score: 0.8967827711313661
Prediction for strontium in zip code 35005 on 2025-11-12: 47.64845999999999

Zip Code: 35005, MSE: 81.54252540810002
Zip Code: 35005, R^2 score: 1.0
Prediction for chlorate in zip code 35005 on 2025-11-12: 66.56536166666666

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 35005, MSE: 0.004899999999999294
Zip Code: 35005, R^2 score: 1.0
Prediction for molybdenum in zip code 35005 on 2025-11-12: 2.699999999999995

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 35005, MSE: 0.03678222440000002
Zip Code: 35005, R^2 score: -573.7222562499993
Prediction for vanadium in zip code 35005 on 2025-11-12: 0.23599000000000003

Zip Code: 35005, MSE: 4.65672325577975
Zip Code: 35005, R^2 score: -1.062301783370784
Prediction for manganese in zip code 35005 on 2025-11-12: 0.9145784999999997

Zip Code: 35005, MSE: 43.09299645627168
Zip Code: 35005, R^2 score: 0.354307515125848
Prediction for HAA5 in zip code 35005 on 2025-11-12: 20.16954955555555

Zip Code: 35005, MSE: 62.07353826315188
Zip Code: 35005, R^2 score: 0.29042820273434755
Prediction for HAA9 in zip code 35005 on 2025-11-12: 25.005654833333328

Zip Code: 35005, MSE: 3.536149026344943
Zip Code: 35005, R^2 score: -0.49063823009554075
Prediction for HAA6Br in zip code 35005 on 2025-11-12: 5.093068722222218

Zip Code: 48858, MSE: 0.08880696931972776
Zip Code: 48858, R^2 score: 0.49413751653319626
Prediction for molybdenum in zip code 48858 on 2025-11-12: 2.1629999999999963

Zip Code: 48858, MSE: 6686.347541122447
Zip Code: 48858, R^2 score: 0.660935723066813
Prediction for strontium in zip code 48858 on 2025-11-12: 266.1666666666667

Zip Code: 48858, MSE: 31012.51951721527
Zip Code: 48858, R^2 score: 0.5476269462873941
Prediction for chlorate in zip code 48858 on 2025-11-12: 251.67166666666665

Zip Code: 48858, MSE: 0.03675337111111109
Zip Code: 48858, R^2 score: -15.539016999999998
Prediction for chromium in zip code 48858 on 2025-11-12: 0.7316333333333332

Zip Code: 48858, MSE: 0.005811347366666692
Zip Code: 48858, R^2 score: -7.435826822580687
Prediction for chromium-6 in zip code 48858 on 2025-11-12: 0.4129733333333336

Zip Code: 48858, MSE: 0.008836000000000078
Zip Code: 48858, R^2 score: 0.0
Prediction for vanadium in zip code 48858 on 2025-11-12: 0.2069999999999996

Zip Code: 48858, MSE: 195.62658958375644
Zip Code: 48858, R^2 score: -0.9685773242877842
Prediction for HAA9 in zip code 48858 on 2025-11-12: 11.389845833333332

Zip Code: 48858, MSE: 53.623383426883
Zip Code: 48858, R^2 score: -0.7128841118272176
Prediction for HAA6Br in zip code 48858 on 2025-11-12: 8.11052916666666

Zip Code: 48858, MSE: 62.41210389526889
Zip Code: 48858, R^2 score: -1.1075972504260823
Prediction for HAA5 in zip code 48858 on 2025-11-12: 4.969304166666672

Zip Code: 48858, MSE: 722.3040272499999
Zip Code: 48858, R^2 score: -6067.506845200586
Prediction for manganese in zip code 48858 on 2025-11-12: 64.7918333333333

Zip Code: 48858, MSE: 449.44000000000074
Zip Code: 48858, R^2 score: 1.0
Prediction for 2-methoxyethanol in zip code 48858 on 2025-11-12: 26.30000000000002

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 48858, MSE: 2.2499999999999844e-06
Zip Code: 48858, R^2 score: 1.0
Prediction for PFBS in zip code 48858 on 2025-11-12: 0.0033000000000000048

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 56633, MSE: 0.32031826777777733
Zip Code: 56633, R^2 score: 1.0
Prediction for HAA9 in zip code 56633 on 2025-11-12: 0.2709333333333333

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 56633, MSE: 0.006084000000000002
Zip Code: 56633, R^2 score: 1.0
Prediction for HAA6Br in zip code 56633 on 2025-11-12: 0.07800000000000001

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 56633, MSE: 0.013448267777777686
Zip Code: 56633, R^2 score: 1.0
Prediction for HAA5 in zip code 56633 on 2025-11-12: 0.2709333333333333

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Zip Code: 98221, MSE: 5.93629434446996
Zip Code: 98221, R^2 score: -0.38455844768978675
Prediction for HAA9 in zip code 98221 on 2025-11-12: 12.290666666666676

Zip Code: 98221, MSE: 5.805016434597076
Zip Code: 98221, R^2 score: -0.7779529661859348
Prediction for HAA5 in zip code 98221 on 2025-11-12: 11.13539444444445

Zip Code: 98221, MSE: 0.0012962816512345587
Zip Code: 98221, R^2 score: 0.9711937410836765
Prediction for HAA6Br in zip code 98221 on 2025-11-12: 1.0294777777777775

Zip Code: 98221, MSE: 39.03750399999998
Zip Code: 98221, R^2 score: 1.0
Prediction for manganese in zip code 98221 on 2025-11-12: 3.2060000000000004

/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:1211: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
  warnings.warn(msg, UndefinedMetricWarning)

Saving top 15 most accurate predictions based on R^2 score out of 39 total graphs
Total graphs generated: 39
{6339: RandomForestRegressor(random_state=42), 35005: RandomForestRegressor(random_state=42), 48858: RandomForestRegressor(random_state=42), 56633: RandomForestRegressor(random_state=42), 98221: RandomForestRegressor(random_state=42)}

<Figure size 640x480 with 0 Axes>
In [ ]:
# print the top 10 zip codes and contaminants that have the highest predicted AnalyticalResultValue

def print_top_10_zipcodes_predicted_contaminants(predicted_AnalyticalResultValue):
    """Prints the top 10 zip codes with the highest predicted contaminants based on predicted AnalyticalResultValue.

    Args:
        predicted_AnalyticalValues (list): A list of tuples containing (zipcode, contaminant, predicted_AnalyticalResultValue).
    """

    # Create a dictionary to store zip codes and their highest predicted MRLs
    zipcode_max_AnalyticalResultValue = {}
    for zipcode, contaminant, predicted_AnalyticalResultValue, collectionDate in predicted_AnalyticalResultValue:
        if zipcode not in zipcode_max_AnalyticalResultValue or predicted_AnalyticalResultValue > zipcode_max_AnalyticalResultValue[zipcode][0]:
            zipcode_max_AnalyticalResultValue[zipcode] = (predicted_AnalyticalResultValue, contaminant, collectionDate)

    # Sort zip codes by highest predicted AnalyticalResultValue in descending order
    sorted_zipcodes = sorted(zipcode_max_AnalyticalResultValue.items(), key=lambda item: item[1][0], reverse=True)

    print("\nTop 10 Zip Codes with Highest Predicted Contaminants and AnalyticalResultValue:")
    for i, (zipcode, (predicted_AnalyticalResultValue, contaminant, collectionDate)) in enumerate(sorted_zipcodes[:10]):
        print(f"{i+1}. Zip Code: {zipcode}, Contaminant: {contaminant}, Predicted AnalyticalResultValue: {predicted_AnalyticalResultValue}, Date: {collectionDate}")


# Assuming 'df' is your DataFrame
print_top_10_zipcodes_predicted_contaminants(predicted_AnalyticalValues)

Top 10 Zip Codes with Highest Predicted Contaminants and AnalyticalResultValue:
1. Zip Code: 48858, Contaminant: strontium, Predicted AnalyticalResultValue: 266.1666666666667, Date: 2025-11-12
2. Zip Code: 6339, Contaminant: chlorate, Predicted AnalyticalResultValue: 239.09, Date: 2025-11-12
3. Zip Code: 35005, Contaminant: chlorate, Predicted AnalyticalResultValue: 66.56536166666666, Date: 2025-11-12
4. Zip Code: 98221, Contaminant: HAA9, Predicted AnalyticalResultValue: 12.290666666666676, Date: 2025-11-12
5. Zip Code: 56633, Contaminant: HAA9, Predicted AnalyticalResultValue: 0.2709333333333333, Date: 2025-11-12
In [ ]:
import matplotlib.pyplot as plt

def plot_top_zipcodes_predicted_contaminants(predicted_AnalyticalResultValue, zipcode_data):

    # Sort predicted AnalyticalResultValue in descending order
    sorted_predictions = sorted(predicted_AnalyticalResultValue, key=lambda item: item[2], reverse=True)

    # Prepare data for the vertical bar graph (using sorted_predictions directly)
    top_zipcodes_contaminants = [(f"{zipcode} - {contaminant} ({predicted_date})")
                                 for zipcode, contaminant, analyticalResultValue, predicted_date in sorted_predictions[:10]]
    top_analyticalResultValues = [analyticalResultValue for zipcode, contaminant, analyticalResultValue, predicted_date in sorted_predictions[:10]]

    # Get zip codes from sorted_predictions (avoiding zipcode_max_analyticalResultValue)
    zipcodes_list = [str(zipcode) for zipcode, _, _, _ in sorted_predictions[:10]]

    # Get unique zip codes from zipcode_data
    unique_zipcodes = [str(zipcode) for zipcode, _ in zipcode_data]

    # Create the vertical bar graph
    plt.figure(figsize=(10, 6))
    plt.bar(top_zipcodes_contaminants, top_analyticalResultValues)
    plt.xlabel("Zip Code - Contaminant - Predicted Date")
    plt.ylabel("Predicted AnalyticalResultValue")
    plt.title(f"Random Forest Regression:\nTop 10 Highest Predicted AnalyticalResultValue in Zip Codes: {', '.join(unique_zipcodes)}\n")
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()
    plt.show()

# Assuming 'predicted_AnalyticalValues' is your list of tuples and 'zipcode_data' is available
plot_top_zipcodes_predicted_contaminants(predicted_AnalyticalValues, zipcode_data)  # Pass zipcode_data
In [ ]:
# prompt: print the number of zipcodes our data has, no duplicates

unique_zipcodes = df['ZIPCODE'].unique()
print(f"Number of unique zipcodes: {len(unique_zipcodes)}")

Number of unique zipcodes: 6

Prediction level heatmaps based on training data

In [ ]:
print(f"Heatmap of all predictions")
heatmap_prediction_data = pd.DataFrame(predicted_AnalyticalValues, columns=['ZIPCODE', 'Contaminant', 'PredictedValue', 'Date'])

# Merge with original DataFrame to get Latitude and Longitude
heatmap_prediction_data = pd.merge(heatmap_prediction_data, df[['ZIPCODE', 'Latitude', 'Longitude']], on='ZIPCODE', how='left')

# Select the necessary columns for the heatmap
heatmap_data = heatmap_prediction_data[['Latitude', 'Longitude', 'PredictedValue']].values.tolist()

# Create a folium map centered on the US
m = folium.Map(location=[37.8, -96.9], zoom_start=4)

# Add a heatmap layer using Latitude, Longitude, and PredictedValue
HeatMap(heatmap_data, min_opacity=0.2, radius=10, blur=10, max_zoom=1).add_to(m)

display(m)
print("\n")

# Create file name and save the map
# file_name = os.path.join(output_dir, f"prediction_heatmap.html")
# save_heatmap_to_file(m, file_name)

Heatmap of all predictions
Make this Notebook Trusted to load map: File -> Trust Notebook

Prediction heatmaps for individual contaminants

In [ ]:
for contaminant, group_data in sorted(heatmap_prediction_data.groupby('Contaminant')):
    print(f"Generating prediction heatmap for Contaminant: {contaminant}\n")

    # Prepare data for this heatmap
    heatmap_data = group_data[['Latitude', 'Longitude', 'PredictedValue']].values.tolist()

    # Create a folium map centered on the US
    m = folium.Map(location=[37.8, -96.9], zoom_start=4)

    # Add a heatmap layer using Latitude, Longitude, and PredictedValue
    HeatMap(heatmap_data, min_opacity=0.2, radius=10, blur=10, max_zoom=1).add_to(m)

    display(m)
    print("\n")

    # Create file name and save the map
    # file_name = os.path.join(output_dir, f"{contaminant}_prediction_heatmap.html")
    # save_heatmap_to_file(m, file_name)

#print(f"Prediction heatmaps saved to the '{output_dir}' directory.")

Generating prediction heatmap for Contaminant: 1,4-dioxane
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Generating prediction heatmap for Contaminant: 2-methoxyethanol
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Generating prediction heatmap for Contaminant: HAA5
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Generating prediction heatmap for Contaminant: HAA6Br
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Generating prediction heatmap for Contaminant: HAA9
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Generating prediction heatmap for Contaminant: PFBS
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Generating prediction heatmap for Contaminant: chlorate
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Generating prediction heatmap for Contaminant: chromium
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Generating prediction heatmap for Contaminant: chromium-6
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Generating prediction heatmap for Contaminant: cobalt
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Generating prediction heatmap for Contaminant: manganese
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Generating prediction heatmap for Contaminant: molybdenum
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Generating prediction heatmap for Contaminant: strontium
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Generating prediction heatmap for Contaminant: vanadium
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